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Two important physical aspects that determine the performance of a running train are the total running resistance that acts on the whole train moving forward, and the available adhesion (utilizable wheel-rail-friction) for propulsion and breaking. Using the measured and available signals, online identification of the current running resistance and available adhesion and also prediction of future values for a distance ahead of the train, is desired. With the aim to enhance the precision of those calculations, this thesis investigates the potential of online identification and prediction utilizing the Extended Kalman Filter.

The conclusions are that problems with observability and sensitivity arise, which result in a need for sophisticated methods to numerically derive the acceleration from the velocity signal. The smoothing spline approximation is shown to provide the best results for this numerical differentiation. Sensitivity and its need for high accuracy, especially in the acceleration signal, results in a demand of higher sample frequency. A desire for other profound ways of collecting further information, or to enhance the models, arises with possibilities of future work in the field.

Mines, construction sites, road construction and quarries are examples of applications where construction equipment are used. In a production chain consisting of several construction machines working together, the work needs to be optimised and coordinated to achieve an environmental friendly, energy efficient and productive production. Recent rapid development within positioning services, telematics and human machine interfaces (HMI) opens up for control of individual machines and optimisation of transport missions where several construction machines co-operate.

The production chain on a work site can be split up in different sub-tasks of which some can be transport missions. Taking off in a transport mission where one wheel loader ("loader" hereinafter) and two articulated haulers ("haulers" hereinafter) co-operate to transport material at a set production rate [ton/h], a method for fuel optimal control is developed. On the mission level, optimal cycle times for individual sub-tasks such as wheel loader loading, hauler transport and hauler return, are established through the usage of Pareto fronts.

The haulers Pareto fronts are built through the development of a Dynamic Programming (DP) algorithm that trades fuel consumption versus cycle time for a road stretch by means of a time penalty constant. Through varying the time penalty constant n number of times, discrete fuel consumption - cycle time values can be achieved, forming the Pareto front. At a later stage, the same DP algorithm is used to generate fuel optimal vehicle speed and gear trajectories that are used as control signals for the haulers. Input to the DP algorithm is the distance to be travelled, road inclination, rolling resistance coefficient and a max speed limit to avoid unrealistic optimisation results.

Thus, a method to describe the road and detect the road related data is needed to enable the optimisation. A map module is built utilising an extended Kalman Filter, Rauch-Tung-Striebel smoother and sensor fusion to merge data and estimate parameters not observable by sensors. The map module uses a model of the vehicle, sensor signals from a GPS or GNSS sensor and machine sensors to establish a map of the road.

The wheel loader Pareto front is based on data developed in previous research combined with Volvo in-house data. The developed optimisation algorithms are implemented on a PC and in an interactive computer tablet based system. A human machine interface is created for the tablet, guiding the operators to follow the optimal control signals, which is speed for the haulers and cycle time for the loader. To evaluate the performance of the system it is tested in real working conditions.

The contributions develop algorithms, set up a demo mission control system and carry out experiments. Altogether rendering in a platform that can be used as a base for a future design of an off-road transport mission control system.

Abstract [en]

Utilising optimal control presents an opportunity to increase the fuel efficiency in an off-road transport mission conducted by an articulated hauler. A human machine interface (HMI) instructing the hauler operator to follow the fuel optimal vehicle speed trajectory has been developed and tested in real working conditions. The HMI implementation includes a Dynamic Programming based method to calculate the optimal vehicle speed and gear shift trajectories. Input to the optimisation algorithm is road related data such as distance, road inclination and rolling resistance. The road related data is estimated in a map module utilising an Extended Kalman Filter (EKF), a Rauch-Tung-Striebel smoother and a data fusion algorithm. Two test modes were compared: (1) The hauler operator tried to follow the optimal vehicle speed trajectory as presented in the HMI and (2) the operator was given a constant target speed to follow. The objective of the second test mode is to achieve an approximately equal cycle time as for the optimally controlled transport mission, hence, with similar productivity. A small fuel efficiency improvement was found when the human machine interface was used.

Utilising optimal control presents an opportunity to increase the fuel efficiency in an off-road transport mission conducted by an articulated hauler. A human machine interface (HMI) instructing the hauler operator to follow the fuel optimal vehicle speed trajectory has been developed and tested in real working conditions. The HMI implementation includes a Dynamic Programming based method to calculate the optimal vehicle speed and gear shift trajectories. Input to the optimisation algorithm is road related data such as distance, road inclination and rolling resistance. The road related data is estimated in a map module utilising an Extended Kalman Filter (EKF), a Rauch-Tung-Striebel smoother and a data fusion algorithm. Two test modes were compared: (1) The hauler operator tried to follow the optimal vehicle speed trajectory as presented in the HMI and (2) the operator was given a constant target speed to follow. The objective of the second test mode is to achieve an approximately equal cycle time as for the optimally controlled transport mission, hence, with similar productivity. A small fuel efficiency improvement was found when the human machine interface was used.

A nonlinear mean value engine model (MVEM) of a two-stroke turbocharged marine diesel engine is developed, parameterized and validated against measurement data. The goal is to have a computationally fast and accurate engine model that captures the main dynamics and can be used in the development of control systems for the newly introduced EGR system. The tuning procedure used is explained, and the result is a six-state MVEM with seven control inputs that capture the main system dynamics.

The purpose of a heat pump is to control the temperature of an enclosed space. This is done by using heat exchange with a heat source, for example water, air, or ground. In the air source heat pump that has been studied during this master thesis, a refrigerant exchanges heat with the outdoor air and with a water distribution system.

The heat pump is controlled through the circuit containing the refrigerant and it is therefore crucial that this circuit is functional. To ensure this, a diagnosis system has been created, to be able to detect and isolate sensor errors. The diagnosis system is based on mathematical models of the refrigerant circuit with its main components: a compressor, an expansion valve, a plate heat exchanger, an air heat exchanger, and a four-way valve. Data has been collected from temperature- and pressure sensors on an air source heat pump. The data has then been divided into data for model estimation and data for model validation. The models are used to create test quantities, which in turn are used by a diagnosis algorithm to determine whether an error has occurred or not.

There are nine temperature sensors and two pressure sensors on the studied air source heat pump. Four fault modes have been investigated for each sensor: Stuck, Offset, Short circuit and Open circuit. The designed diagnosis system is able to detect all of the investigated error modes and isolate 40 out of 44 single errors. However, there is room for improvement by constructing more test quantities to detect errors and decouple more fault modes. To further develop the diagnosis system, the existing models can be improved and new models can be created.

More restrictive emission legislations, rising fuel prices and the realisation that oil is a limited resource have lead to the emergence of the hybrid electric vehicles.To fully utilise the potential of the hybrid electric vehicles, energy management strategies are needed. The main objective of the strategy is to ensure that the limited electric energy is utilised in an efficient manner.This thesis develops and evaluates an optimisation based energy management strategy for plug-in hybrid electric vehicles. The optimisation methods used are based on a dynamic programming and ECMS approach. The strategy is validated against Vsim, Volvo Cars' performance and fuel consumption analysis tool as well as against strategies where parts of the optimisation is replaced by logic. The results show that the developed strategy consumes less fuel both compared to the corresponding Vsim strategy and the logic strategies.

This paper describes a solution to the Advanced Diagnosis and Prognostics testbed (ADAPT) diagnosis benchmark problem. One main objective was to study and discuss how engineering students, with no diagnosis research background, would solve a challenging diagnosis problem. The study was performed within the framework of a final year project course for control engineering students. A main contribution of the work is the discussion on the development process used by the students. The solution is based on physical models of components and includes common techniques from control theory, like observers and parameter estimators, together with established algorithms for consistency based fault isolation. The system is fully implemented in C++ and evaluated, using the DXC software platform, with good diagnosis performance.

When further demands are placed on emissions and performance of cars, trucks and busses, the vehicle manufacturers are looking to have cheap ways to evaluate their products for specific customers' needs. Using simulation tools to quickly compare use cases instead of manually recording data is a possible way forward. However, existing traffic simulation tools do not provide enough detail in each vehicle for the driving to represent real life driving patterns with regards to road features.

For the purpose of this thesis data has been recorded by having different people drive a specific route featuring highway driving, traffic lights and many curves. Using this data, models have then been estimated that describe how human drivers adjust their speed through curves, how long braking distances typically are with respect to the driving speed, and the varying deceleration during braking sequences. An additional model has also been created that produces a speed variation when driving on highways. In the end all models are implemented in Matlab using a traffic control interface to interact with the traffic simulation tool SUMO.

The results of this work are promising with the improved simulation being able to replicate the most significant characteristics seen from human drivers when approaching curves, traffic lights and intersections.

To evaluate driver perception of a vehicle powertrain a moving base simulator is a well-established technique. We are connecting the moving base simulator Sim III, at the Swedish National Road and Transport Research Institute with a newly built chassis dynamometer at Vehicular Systems, Linköping University. The purpose of the effort is to enhance fidelity of moving base simulators by letting drivers experience an actual powertrain. At the same time technicians are given a new tool for evaluating powertrain solutions in a controlled environment. As a first step the vehicle model from the chassis dynamometer system has been implemented in Sim III. Interfacing software was developed and an optical fiber covering the physical distance of 500 m between the facilities is used to connect the systems. Further, a pedal robot has been developed that uses two linear actuators pressing the accelerator and brake pedals. The pedal robot uses feedback loops on accelerator position or brake cylinder pressure and is controlled via an UDP interface. Results from running the complete setup showed expected functionality and we are successful in performing a driving mission based on real road topography data. Vehicle acceleration and general driving feel was perceived as realistic by the test subjects while braking still needs improvements. The pedal robot construction enables use of a large set of cars available on the market and except for mounting the brake pressure sensor the time to switch vehicle is approximately 30 minutes.

This thesis covers the reconstruction and the redesign of the modeling library VehProLib,which is constructed in the modeling language Modelica with help of the modeling toolWolfram SystemModeler. The design choices are discussed and implemented. This thesisalso includes the implementation of a turbocharger package and an initial study of the justificationof the ideal gas law in vehicle modeling. The study is made with help of Van derWaals equation of states as a reference of non-ideal gas model. It will be shown that for themean-value-engine-model, the usage of ideal gas law is justified.

In modern heavy duty trucks the battery is a central component. Its traditional role as an energy source for engine cranking has been extended to include powering a number of elec- trical components on the truck, both during driving and during standstill. As a consequence of this it is important to know how much a battery in use has aged and lost in terms of ca- pacity and power output. The difficulty in measuring these factors on a battery in use causes problem, since heavy duty truck batteries are often replaced too early or too late, leading to unnecessary high replacement costs or truck standstill respectively.

The overall goal of the effort, of which this thesis is a part, is to use a model of the cranking behaviour of a heavy duty truck engine, which depends on the battery condition, to estimate the ageing and wear of a heavy duty truck battery. This thesis proposes a modelling approach to model the components involved in engine cranking.

In the thesis work, system identification is made of the systems forming part of the cranking of a heavy duty truck engine. These components are the starter battery, the starter motor and its electrical circuit and the internal combustion engine. Measurement data has been provided by Scania AB for the evaluation of the models. The data has been collected from crankings of a heavy duty diesel engine at different temperatures and battery charge levels. For every cranking lapse the battery voltage and current have been measured as well as the engine rotational speed.

A starter battery model is developed and evaluated. The resulting battery model is then incorporated into two different engine cranking models, Model 1 and Model 2, including a starter motor model and an internal combustion engine model apart form the battery model. The two cranking models differ in several aspects and their differences and resulting evalu- ations are discussed.

The battery model is concluded to be sufficiently accurate during model verification, however the two cranking models are not. Model 2 is verified as more correct in in its output than Model 1, but neither is sufficiently accurate for their purpose. The conclusion is drawn that the modelling approach is sound but development of Model 2 is needed before the model can be used in model-based condition estimation.

The controls for a parallel hybrid electric truck are optimized using numerical optimal control. Trade-offs between catalyst light-off times, NOx emission and fuel consumption have been investigated for cold starts at two operating points, as well as temperature differences between conventional and hybrid powertrains during WHTC (World Harmonized Transient Cycle). A model describing the temperature dynamics of the aftertreatment system is implemented as well as temperature-based deNOx performance for both Cu-Zeolite and Fe-Zeolite catalysts. Control is performed in a piecewise linear fashion, resulting in a total of 23 states including control signals. It is shown that high temperatures can be a larger threat to catalyst performance when running the WHTC than low temperatures, for both conventional and hybrid powertrains. Furthermore, decreasing the light-off time of the catalyst does not always lead to decreased NOx emission, instead there is a trade-off between light-off time and NOx emission. It is found that there are controls that will realize decreased NOx emission for a hybrid truck during cold starts at the expense of increased fuel consumption.

Due to governmental requirements on low exhaust gas emissions and the drivers request of fast response, it is important to be able to control the gas flow in a spark ignited engine accurately. The air into the cylinder is directly related to the torque generated by the engine. The technique with recirculation of exhaust gases (EGR) affect the air flow into the cylinder and increase the complexity of the control problem. In this thesis a mean value model for a spark ignited engine has been created. The basis was a diesel model from Linköping University that has been modified and parameterized with data from a test cell. The model has been used to study the gas exchange system with respect to the dynamic behaviors and nonlinearities that occur when the three actuators (throttle, wastegate and EGR-valve) are changed. Based on this analysis, some different control strategies have been developed and tested on the model. The presented results show that different control strategies give different behaviors and there is a trade-off between fast torque response and high precision for controlling the EGR-ratio. A control strategy is proposed containing two main feedback loops, prefiltering of the reference signal and a feedforward part.

A model for the thermal part of an ionization signal is presented that connects the ionization current to cylinder pressure and temperature in a spark ignited internal combustion engine. One strength of the model is that, after calibration, it has only two free parameters: burn angle and initial kernel temperature. By fitting the model to a measured ionization signal, it is possible to estimate both cylinder pressure and temperature, where the pressure is estimated with good accuracy. The model approach is validated on engine data. Cylinder pressure and ionization current data were collected on a Saab four-cylinder spark ignited engine for a variation in ignition timing and air-fuel ratio. The main result is that the parametrized ionization current model can be used to estimating combustion properties as pressure, temperature, and content of nitric oxides based on measured ionization currents. The current status of the model is suitable for off-line analysis of ionization currents and cylinder pressure. This ionization current model not only describes the connection between the ionization current and the combustion process, but also offers new possibilities for engine management system to control the internal combustion engine.

The combustion stability of a direct injected spark ignited engine depends on the injection timing and it is desirable to have a controller that minimizes the combustion variability. A novel approach for determining combustion stability in stratified mode is presented that rely on the ionization current and enables closed loop control of the injection timing. The coefficient of variation for IMEP is used as a measure of combustion stability and a connection between maximum torque and low combustion variability is pointed out. The coefficient of variation of the ion current integral is well correlated with the coefficient of variation for IMEP. Furthermore, it is shown how the integral of the ion current together with COV(ion integral) can be used to determine the combustion stability and to distinguish high combustion stability from misfire.

This paper describes a vacuum-decay based evaporative leak detection procedure for vehicle fuel systems. A physical model for an evaporative system is proposed containing parts for fuel evaporation, leakage flow and canister flow. Two methods for detecting evaporative leakages based on the model is presented. Both methods can detect a 0.5 mm diameter leakage in a laboratory environment. Keywords: purge system, fault diagnosis, fault detection, model based diagnosis 1. INTRODUCTION According to regulations for emissions from vehicles, fuel vapor leakage from the fuel tank must be detected. Fuel vapor is always generated in the fuel tank, the amount depends on ambient conditions like temperature and movement of the tank. Filling fuel also causes extra vapor to be generated. The fuel vapor may cause an over pressure that may push vapor out of the tank. Also, as fuel is consumed an under-pressure develops in the tank and it is required to level the fuel-tank gas pressure with ambi.

The increasing complexity of today’s vehicles gives drivers help with everything from adaptive cruisecontrol to warning lights for low fuel level. But the increasing functionality also increases the risk offailures in the system. To prevent system failures, different safety analytic methods can be used, e.g.,fault trees and/or FMEA-tables. These methods are generally performed manually, and due to thegrowing system size the time spent on safety analysis is growing with increased risk of human errors. If the safety analysis can be automated, lots of time can be saved.

This thesis investigates the possibility to generate fault trees from safety requirements as wellas which additional information, if any, that is needed for the generation. Safety requirements are requirements on the systems functionality that has to be fulfilled for the safety of the system to be guaranteed. This means that the safety of the truck, the driver, and the surroundings, depend on thefulfillment of those requirements. The requirements describing the system are structured in a graphusing contract theory. Contract theory defines the dependencies between requirements and connectsthem in a contract structure.

To be able to automatically generate the fault tree for a system, information about the systems failure propagation is needed. For this a Bayesian network is used. The network is built from the contract structure and stores the propagation information in all the nodes of the network. This will result in a failure propagation network, which the fault tree generation will be generated from. The failure propagation network is used to see which combinations of faults in the system can violate thesafety goal, i.e., causing one or several hazards. The result of this will be the base of the fault tree.

The automatic generation was tested on two different Scania systems, the fuel level displayand the dual circuit steering. Validation was done by comparing the automatically generated trees withmanually generated trees for the two systems showing that the proposed method works as intended. The case studies show that the automated fault tree generation works if the failure propagationinformation exists and can save a lot of time and also minimize the errors made by manuallygenerating the fault trees. The generated fault trees can also be used to validate written requirementsto by analyzing the fault trees created from them.

For any crankcase scavenged two-stroke engine, the fuel dynamics is not easily predicted. This is due to the fact that the fuel has to pass the crankcase volume before it enters the combustion chamber. This thesis is about the development of a model for fuel dynamics in the crankcase of a small crankcase scavenged two-stroke engine that gives realistic dynamic behavior.

The crankcase model developed in this thesis has two parts. One part is a model for wall wetting and the other part is a model for concentration of evaporated fuel in the crankcase. Wall wetting is a phenomenon where fuel is accumulated in fuel films on the crankcase walls. The wall wetting model has two parameters that have to be tuned. One is for the fraction of fuel from the carburetor that is not directly evaporated and one parameter is for the evaporation time of the fuel film.

The thesis treats tuning of these parameters by running the model with input data from measurements. Since not all input data are possible to measure, models for these inputs are also needed. Hence, development of simple models for air flows, fuel flow, gas mixing in the exhaust and the behavior of the λ-probe used for measurements are also treated in this thesis.

The parameter estimation for the crankcase model made in this thesis results in parameters that corresponds to constant fraction of fuel from the carburetor that evaporates directly and a wall wetting evaporation rate that increases with increasing engine speed. The parameter estimation is made with measurements at normal operation and three specific engine speeds. The validity of the model is limited to these speeds and does not apply during engine heat-up.

The model is run and compared to validation data at some different operation conditions. The model predicts dynamic behavior well, but has a bias in terms of mean level of the output λ. Since this mean value depends on the relation between input air and fuel flow, this bias is probably an effect of inaccuracy in the simple models developed for these flows.

In unmanned aerial systems an autopilot controls the vehicle without human interference. Modern autopilots use an inertial navigation system, GPS, magnetometers and barometers to estimate the orientation, position, and velocity of the aircraft. In order to make correct decisions the autopilot must rely on correct information from the sensors.

Fault diagnosis can be used to detect possible faults in the technical system when they occur. One way to perform fault diagnosis is model based diagnosis, where observations of the system are compared with a mathematical model of the system. Model based diagnosis is a common technique in many technical applications since it does not require any additional hardware. Another way to perform fault diagnosis is hardware diagnosis, which can be performed if there exists hardware redundancy, i.e. a set of identical sensors measuring the same quantity in the system.

The main contribution of this master thesis is a model based diagnosis system for a fixed wing UAV autopilot. The diagnosis system can detect faults in all sensors on the autopilot and isolate faults in vital sensors as the GPS, magnetometer, and barometers. This thesis also provides a hardware diagnosis system based on the redundancy obtained with three autopilots on a single airframe. The use of several autopilots introduces hardware redundancy in the system, since every autopilot has its own set of sensors. The hardware diagnosis system handles faults in the sensors and actuators on the autopilots with full isolability, but demands additional hardware in the UAV.

In turbocharged engines with wastegate the exhaust pressure can change rapidly. Two methods to estimate the exhaust manifold pressure are compared for diagnosis of wastegate and turbocharger of a spark-ignited engine. One relies on the first law of thermodynamics and produces changes in exhaust manifold pressure. The second uses a model of the mass of remaining exhaust gases in the cylinder and results in absolute estimations of the exhaust manifold pressure. They does not require any extra sensors in the exhaust system after the calibration. Estimates of the exhaust manifold pressure relies on information from an air-to-cylinder observer and a static map. The exhaust manifold pressure estimators are compared using a series of wastegate steps on a turbocharged SAAB 2.3 dm^3 SI-engine. The comparison showed that the method based on the first law of thermodynamics was best suited for diagnosis purposes since it was least sensitive to model errors.

On turbocharged spark-ignited (SI) engines with wastegate the position of the wastegate changes the exhaust manifold pressure. A secondary effect of this is that the residual gas mass trapped inside the cylinder at exhaust valve closing changes and causes the volumetric efficiency to change. The volumetricefficiency is used to estimate air-mass-to-cylinder which is important for good air/fuel ratio control.

Air-mass to-cylinder is not directly measurable so observers for air-mass flow to the cylinder are therefore often proposed. For observers with one state for intake manifold pressure and proportional feed-back from measured state, there is a tradeoff whether to estimate intake manifold pressure or air-mass-to-cylinder. A new nonlinear air-mass-to-cylinder observer is suggested with two states: one for intake manifold pressure and one for the in-cylinder air-mass offset compared to expected using the volumetric efficiency.

The exhaust manifold pressure can change rapidly in an engine with wastegate. A method to estimate the exhaust manifold pressure is presented for diagnosis of wastegate and turbocharger on SI-engines. It does not use any extra sensors in the exhaust system after the calibration. The exhaust manifold pressure estimator is validated using a series of wastegate steps. The exhaust pressure estimation is designed for steady-state conditions and the validation shows that it works well and converges within 1 to 4 seconds.

Finally a method to detect leakages in the exhaust manifold is suggested. Leakage detection before the three way catalyst is important since untreated emissions leak out and since, due to standing waves in the exhaust system, air can leak in and disturb the air/fuel ratio controller. To extend the operating region for the detection, the proposed method utilizes both information on leaks out of the manifold and information on presence of oxygen in the exhaust manifold.

Abstract: Air-fuel control on turbocharged (TC) SI-engines require precise prediction of the cylinder air-charge (CAC). Using an observer it is possible to both estimate the necessary system states and to provide a framework to design the necessary CAC feedforward controller. Here a mean value engine model of a TC SI-engine is used to develop an observer. The output of the observer is fed as an initial condition to a predictor which is used for feedforward of the CAC for air-fuel control. The resulting controller is experimentally validated on a SAAB 2.0 dm^3 TC engine using tip-in and tip-out transients. The results show that the excursions in lambda are less than 5%.

Air-fuel control on turbocharged (TC) SI-engines require precise prediction of the cylinder air-charge (CAC). Using an observer it is possible to both estimate the necessary system states and to provide a framework to design the necessary CAC feedforward controller. Here a mean value engine model of a TC SI-engine is used to develop an observer. The output of the observer is fed as an initial condition to a predictor which is used for feedforward of the CAC for air-fuel control. The resulting controller is experimentally validated on a SAAB 2.0 dm3 TC engine using tip-in and tip-out transients. The results show that the excursions in Λ are less than 5%

Recent research has shown that control of the oxygen content in the catalyst has potential to further reduce the emissions from spark ignited engines. This gives rise to a cascade structure where an outer loop influences an inner loop. Different ways of augmenting the inner loop, a traditional PI-feedback controller based on feedback from the binary oxygen sensor, are studied. The SI-engine constraints on the control, such as low emissions and drive ability, are considered in the evaluation of the controllers. The result is that a delayed switching of the sensor is needed to control the oxygen content in the TWC using binary sensor feedback.

Environmental regulations and drivability issues are driving forces in the development of control systems for automotive engines. Precise control of the air and fuel is fundamental for achieving the goals. Furthermore, the architecture for the controller plays a central role in how the goals are achieved.

A comparison is made between two conventional controller structures and a model based structure. The performance of the different control structures is evaluated on a simulation model. To point out the differences the evaluation is concentrated to transient conditions where a step in throttle angle is used as input to the system. In addition, connections between controllers and the engine model is discussed.

A formulation of an offline motion-planning method for avoidance maneuvers based on a lane-deviation penalty function is proposed,which aims to decrease the risk of a collision by minimizing the time when a vehicle is outside of its own driving lane in the case ofavoidance maneuvers. The penalty function is based on a logistic function. The method is illustrated by computing optimal maneuversfor a double lane-change scenario. The results are compared with minimum-time maneuvers and squared-error norm maneuvers. Thecomparison shows that the use of the considered penalty function requires fewer constraints and that the vehicle stays less time in theopposing lane. The similarity between the obtained trajectories for different problem configurations was noticed. This property couldbe used in the future for predicting an intermediate trajectory online from a sparse data set of maneuvers.

To decrease the complexity of motion-planning optimizations, a segmentation and merging strategy for maneuvers is proposed. Maneuvers that are at-the-limit of friction are of special interest since they appear in many critical situations. The segmentation pointsare used to set constraints for several smaller optimizations for parts of the full maneuver, which later are merged and compared withoptimizations of the full maneuver. The technique is illustrated for a double lane-change maneuver.

The issue of residual generation using structural analysis has been studied by several authors. Structural analysis does not permit to generate the analytical expressions of residuals since the model of the system is abstracted by its structure. However, it determines the set of constraints from which residuals can be generated and it provides the computation sequence to be used. This paper presents and compares four recently proposed algorithms that solve this problem.

Coiled coils with defined assembly properties and dissociation constants are highly attractive components in synthetic biology and for fabrication of peptide-based hybrid nanomaterials and nanostructures. Complex assemblies based on multiple different peptides typically require orthogonal peptides obtained by negative design. Negative design does not necessarily exclude formation of undesired species and may eventually compromise the stability of the desired coiled coils. This work describe a set of four promiscuous 28-residue de novo designed peptides that heterodimerize and fold into parallel coiled coils. The peptides are non-orthogonal and can form four different heterodimers albeit with large differences in affinities. The peptides display dissociation constants for dimerization spanning from the micromolar to the picomolar range. The significant differences in affinities for dimerization make the peptides prone to thermodynamic social self-sorting as shown by thermal unfolding and fluorescence experiments, and confirmed by simulations. The peptides self-sort with high fidelity to form the two coiled coils with the highest and lowest affinities for heterodimerization. The possibility to exploit self-sorting of mutually complementary peptides could hence be a viable approach to guide the assembly of higher order architectures and a powerful strategy for fabrication of dynamic and tuneable nanostructured materials.

Nonlinear model predictive control (NMPC) has become increasingly important for today’s control engineers during the last decade. In order to apply NMPC a nonlinear optimal control problem (NOCP) must be solved which needs a high computational effort.

State-of-the-art solution algorithms are based on multiple shooting or collocation algorithms; which are required to solve the underlying dynamic model formulation. This paper describes a general discretization scheme applied to the dynamic model description which can be further concretized to reproduce the mul-tiple shooting or collocation approach. Furthermore; this approach can be refined to represent a total collocation method in order to solve the underlying NOCP much more efficiently. Further speedup of optimization has been achieved by parallelizing the calculation of model specific parts (e.g. constraints; Jacobians; etc.) and is presented in the coming sections.

The corresponding discretized optimization problem has been solved by the interior optimizer Ipopt. The proposed parallelized algorithms have been tested on different applications. As industrial relevant application an optimal control of a Diesel-Electric power train has been investigated. The modeling and problem description has been done in Optimica and Modelica. The simulation has been performed using OpenModelica. Speedup curves for parallel execution are presented.

This thesis is about the development of a function that assists the driver of a heavy vehicle to do an estimation over the possibilities to overtake a preceding heavy vehicle. The function utilizes Look-Ahead and vehicle-to-vehicle communication to do a calculation of the distance between the vehicles in some road distance ahead. Consequently the report also contains an investigation of what data that is needed to be known about a vehicle to be able to do a satisfying estimation about this vehicle. The most vital problem is to estimate what velocity the vehicle will get in an uphill/downhill slope. A Simulink model is developed to simulate the function with two independent vehicles. Real tests are also performed to evaluate the velocity estimation part of the function.

As vehicle testing on existing vehicles is both time and resource consuming, the work of testing safety algorithms on vehicle is desired to be made more efficient. Therefore the goal of this thesis is to study and develop a vehicle simulation model that can simulate desired dynamics of existing and non-existing vehicles.

The developed model consist of two areas of application: slow dynamics and vibrational dynamics. These areas are developed and validated using different methods, but as a part of the simulator, they are to be simulated together.

For the slow, low frequency, vehicle motion, a three state transient motion model is derived and examined. The possibility of parametrisation is studied and performed using prediction error minimisation.

For the vibration, high frequency model, a combination of a linear quarter car model with wheel motion is used to estimate road vibration characteristics. The modelled road is used to simulate the vehicle behaviour. The suggested methods regarding the vibration modelling and road estimation are performed using power spectral density as the road is not known determinately. Wheel speeds are used to study the power spectral densities as they are available at high sampling frequencies.

The available tools and sensors used during this thesis are limited to existing vehicle sensors and GPS signals. The effect of this limitation is studied and the results are discussed.

Diagnosis based on vibration analysis is a method that has many benefits to offer. It is easy to implement the method on existing transmissions by attaching accelerometers outside the gearbox housing. If you have knowledge of the gearbox geometry, such as number of tooth on the gears and types of bearings, and any unwanted frequencies can be filtered out a good estimation of the gearbox condition can be achieved. In this thesis a number of condition indicators have been tested to identify and isolate different faults that may appear. All analysing have been done in the time domain on different synchronously averaged signals. The condition indicators have been used together with diagnosis theory from the division of Vehicular systems to create a diagnosis system able to find faults on a number of modelled signals.

A turbocharger’s performance is measured in a gas stand in order to provide information of the components characteristics. The measurement procedure is a very time consuming process and it is thus desired to make it more time-efficient.

To allow for development of an enhanced control strategy used during the measurements, a 0D model of a gas stand is developed. The physical gas stand components are modeled and validated against measurements, all showing a reasonable result. Turbocharger heat transfers are investigated and modeled using a lumped capacitance approach. The heat transfer models shows approximative results when comparing with measurements which is explained by the lack of temperature measurement made on the bearing housing.

When the complete gas stand model is validated against measurements, an improvement of the measurement procedure is examined. By adding an idealized heat source with the possibility to heat the compressor housing, it is possible to reduce the time it takes to reach an equilibrium when switching between two steady state operating points.

The continuous challenge to decrease emissions, sensor costs and fuel consumption in diesel engines is battled in this thesis. To reach higher goals in engine efficiency and environmental sustainability the prediction of engine states is essential due to their importance in engine control and diagnosis. Model output will be improved with help from sensors, advanced mathematics and non linear Kalman filtering. The task consist of constructing non linear Kalman Filters and to adaptively weight measurements against model output to increase estimation accuracy. This thesis shows an approach of how to improve estimates by nonlinear Kalman filtering and how to achieve additional information that can be used to acquire better accuracy when a sensor fails or to replace existing sensors. The best performing Kalman filter shows a decrease of the Root Mean Square Error of 75 % in comparison to model output.

Functionality in the automotive industry is becoming more complex, withcomplex communication networks between control systems. Information isshared among many control systems and extensive testing ensures high quality.

Degradations testing, that has the objective to test functionality with some faultpresent, is performed on single control systems, but is not frequently performed on the entire electrical system. There is a wish for testing degradation automatically on the complete electrical system in a so called Hardware-In-the-Loop laboratory.

A technique is needed to perform these tests on a regular basis.Problems with testing degradation in complex communication systems will bedescribed. Methods and solutions to tackle these problems are suggested, thatfinally end up with two independent test strategies. One strategy is suited to test degradation on new functionality. The other strategy is to investigate effects in the entire electrical system. Both strategies have been implemented in a Hardware-In-the-Loop laboratory and evaluated.

There is currently a strongly growing interest in obtaining optimal control solutions for vehicle manoeuvres, both in order to understand optimal vehicle behaviour and, perhaps more importantly, to devise improved safety systems, either by direct deployment of the solutions or by including mimicked driving techniques of professional drivers. However, it is non-trivial to find the right combination of models, optimisation criteria, and optimisation tools to get useful results for the above purposes. Here, a platform for investigation of these aspects is developed based on a state-of-the-art optimisation tool together with adoption of existing vehicle chassis and tyre models. A minimum-time optimisation criterion is chosen for the purpose of gaining an insight into at-the-limit manoeuvres, with the overall aim of finding improved fundamental principles for future active safety systems. The proposed method to trajectory generation is evaluated in time-manoeuvres using vehicle models established in the literature. We determine the optimal control solutions for three manoeuvres using tyre and chassis models of different complexities. The results are extensively analysed and discussed. Our main conclusion is that the tyre model has a fundamental influence on the resulting control inputs. Also, for some combinations of chassis and tyre models, inherently different behaviour is obtained. However, certain variables important in vehicle safety-systems, such as the yaw moment and the body-slip angle, are similar for several of the considered model configurations in aggressive manoeuvring situations.

There is currently a strongly growing interest in obtaining optimal control solutions for vehicle maneuvers, both in order to understand optimal vehicle behavior and to devise improved safety systems, either by direct deployment of the solutions or by including mimicked driving techniques of professional drivers. However, it is nontrivial to find the right mix of models, formulations, and optimization tools to get useful results for the above purposes. Here, a platform is developed based on a stateof-the-art optimization tool together with adoption of existing vehicle models, where especially the tire models are in focus. A minimum-time formulation is chosen to the purpose of gaining insight in at-the-limit maneuvers, with the overall aim of possibly finding improved principles for future active safety systems. We present optimal maneuvers for different tire models with a common vehicle motion model, and the results are analyzed and discussed. Our main result is that a few-state singletrack model combined with different tire models is able to replicate the behavior of experienced drivers. Further, we show that the different tire models give quantitatively different behavior in the optimal control of the vehicle in the maneuver.

The requirements on fuel consumption and emissions for passenger cars are getting stricter every year. This has forced the vehicle industry to look for ways to improve the performance of the driveline. With the increasing focus on electrification, a common method is to combine an electrical driveline with a conventional driveline that uses a petrol or diesel engine, thus creating a hybrid electric vehicle. To fully be able to utilise the potential of the driveline in such a vehicle, an efficient energy management strategy is needed. This thesis describes the development of an efficient route-based energy management strategy. Three different optimisation strategies are combined, deterministic dynamic programming, equivalent consumption minimisation strategy and convex optimisation, together with segmentation of the input data. The developed strategy shows a decrease in computational time with up to more than one hundred times compared to a benchmark algorithm. When implemented in Volvo's simulation tool, VSim, substantial fuel savings of up to ten percent is shown compared to a charge-depleting charge-sustain strategy.

Fault diagnosis is becoming increasingly important for many technical systems. This is for example true in automotive vehicles where fault diagnosis is needed due to economic reasons such as efficient repair and fault prevention, and legislations that mainly deal with safety and pollution. The objective for a diagnostic system is to detect and isolate faults in the system. A diagnostic system consists of several specialized parts, for example residual generators, diagnoses calculation, and communication with other systems.

In embedded systems with dozens of electronic control units that individually states local diagnoses, it can be computationally expensive to find which combination of local diagnoses that points at the correct set of faulty components. A distributed method is proposed where local diagnoses are extended using networked information. The extension is done thru the sharing of local conflicts or local diagnoses between the electronic control units. The number of global diagnoses grows with the number of local diagnoses. Therefore, an algorithm is presented that from the local diagnoses calculates the more likely global diagnoses. This restriction to the more likely diagnoses is sometimes appropriate since there are limitations in processing power, memory, and network capacity.

A common approach to design diagnostic systems is to use residual generators, where each residual generator is sensitive to some faults. A method is presented that constructs residual generators from sets of overdetermined model equations, such that simulation can be used to determine if the residual is zero or not. The method thus avoids the need to analytically transform the set of equations into some specific residual generator form. It can also utilize smaller sub sets of equations like minimally overdetermined sets, and it can further take advantage of object-oriented simulation tools.

To improve safety, reliability, and efficiency of automotive vehicles and other technical applications, embedded systems commonly use fault diagnosis consisting of fault detection and isolation. Since many systems are constructed as distributed embedded systems including multiple control units, it is necessary to perform global fault isolation using for example a central unit. However, the drawbacks with such a centralized method are the need of a powerful diagnostic unit and the sensitivity against disconnections of this unit.

Two alternative methods to centralized fault isolation are presented in this thesis. The first method performs global fault isolation by a istributed sequential computation. For a set of studied systems, themethod gives, compared to a centralizedmethod, amean reduction inmaximumprocessor load on any unitwith 40 and 70%for systems consisting of four and eight units respectively. The second method instead extends the result of the local fault isolation performed in each unit such that the results are globally correct. By only considering the components affecting each specific unit, the extended result in each agent is kept small. For a studied automotive vehicle, the second method gives, compared to a centralized method, a mean reduction in the sizes of the results and the maximum processor load on any unit with 85 and 90% respectively.

To perform fault diagnosis, diagnostic tests are commonly used. If the additional evaluation of tests can not improve the fault isolation of a component then the component is ready. Since the evaluation of a test comes with a cost in for example computational resources, it is valuable to minimize the number of tests that have to be evaluated before readiness is achieved for all components. A strategy is presented that decides in which order to evaluate tests such that readiness is achieved with as few evaluations of tests as possible.

Besides knowing how fault diagnosis is performed, it is also interesting to assess the effect that fault diagnosis has on for example safety. Since fault tree analysis often is used to evaluate safety, this thesis contributes with a systematic method that includes the effect of fault diagnosis in fault trees. The safety enhancement due to the use of fault diagnosis can thereby be analyzed and quantified.

A first implementation of a mean value engine model (MVEM) of a Heavy Duty Diesel (HDD) engine is described in this report. Framework and sub models are described. Where applicable ISO standards are followed. Verification against static measurements shows maximum model errors of about 6 % for mass flow and inlet/exhaust manifold pressures.